Green Port Transformation and Heavy-Duty Forklift Selection a Fermatean Fuzzy MCDM Framework

With the increase in global trade volume, the efficiency of container transportation and port operations, as well as the diversity offered at the service level, have become among the most critical factors determining the competitiveness of supply chains. In this context, Container Freight Stations (CFS), where container loading, unloading, inspection, and temporary storage processes are carried out, play a strategic role in the continuity of terminal operations [1]. Today, port operators have made improving environmental performance, energy efficiency, and carbon footprint among their core objectives in line with green port policies [2]. This transformation process directly impacts the selection of operational equipment for CFS yards.

Handling heavy tonnage cargo in CFS yards requires a high level of equipment and technology for operational safety, efficiency, and sustainability [3]. The ability to handle heavy tonnage cargo is directly determined by the technical and functional specifications of the forklifts used in the field. Forklifts are among the essential equipment for cargo handling in CFS yards. However, each forklift model; they vary in terms of lifting capacity, energy type, maneuverability, hydraulic system, and ergonomic design. This diversity transforms the selection process from an evaluation limited to technical parameters to a multi-dimensional decision-making problem centered on sustainability.

In this context, the appropriate selection of forklifts to be used in the field depends not only on physical capacity but also on Environmental, Social, and Governance (ESG)-based criteria. In CFS operations, particularly where loads exceeding 25 tons are handled, the wrong forklift selection can lead to negative consequences such as equipment failures, operational delays, fuel waste, and occupational safety risks. Therefore, forklift selection is not only a technical investment decision; it has also become an integral component of sustainable operational strategies aligned with ports' green transformation policies.

ESG has become a vital component in establishing policies for sustainable development in the maritime logistics and port sectors. ESG frameworks provide ports with a multidimensional structure in terms of improving environmental performance, strengthening social responsibility, and enhancing transparency and accountability in governance. Investigations have shown that investor expectations are increasingly focused not only on financial returns but also on conformance with ESG practices that shape strategic decisions and affect resilience within supply chains [4].

Current studies of ESG performance in ports indicate that there is a need to incorporate environmental impacts, energy use, waste management, social responsibility practices, and quality of governance into integrated assessment systems. Quantitative ESG performance evaluation models have been developed that enable comparison among international ports based on structured sustainability benchmarking and informed responsible investment decisions. Such models emphasize the ability of ESG indicators to capture the environmental and social performance profile of ports as a structured basis for evaluating sustainability maturity across different port systems [5].

Literature also explores the impact of ESG practices on port resilience. System dynamics models indicate that ESG-driven strategies enhance the ability of ports to bounce back from disruptions, reduce risks, and maintain operational continuity. Evidence exists that improved environmental performance, along with enhanced social sustainability measures and amplified governance mechanisms, combine to realize better levels of port resilience and stable patterns of long-term development [6].

Studies on ESG in port management show that ports benefit from clear frameworks that support their sustainability efforts. Such frameworks incorporate policies, programs, processes, and partnerships to support the systematic ESG integration approach. In fact, research done in the Brazilian context has demonstrated how collaborative guides to sustainability enable ports to implement ESG principles with international alignment while responding to local regulatory and operational peculiarities, creating an organizational culture favorable to long-term sustainable development [7].

Empirical research on Chinese ports also uncovers the role of ESG indicators in the evaluation of sustainable port development. These studies apply analytical techniques like DEA and CRITIC weighting to analyze environmental efficiency, carbon emission performance, energy use, social responsibility, and governance quality. The results prove that ESG metrics provide a full-scale and comparable analysis of port performance and permit the identification of critical strengths and weaknesses at the environmental, social, and governance levels [8], [9]. This kind of empirical ESG-based evaluation represents an important tool for guiding ports in pursuing sustainable strategic objectives.

Other studies examine organizational and operational issues in ESG practice adoption by ports. These works have identified financial constraints, institutional capacity, regulatory compliance needs, and stakeholder expectations as important factors that shape ESG adoption processes. As the port sector continues to evolve, effective strategic management of ESG factors is increasingly recognized as central to determining sustainable growth, good governance, and organizational legitimacy. The broader ESG literature within the maritime sector stresses the interconnectedness of environmental sustainability, social responsibility, and corporate governance. Review documents indicate that ESG applications influence corporate behavior, stakeholder engagement, reporting quality, and value creation over the longer term. Additionally, it has been shown that ESG practices influence industry-wide standards regarding transparency, risk management, and ethical governance-things that confirm the potential for transformation in ESG applications within the maritime domain [10].

Lastly, a review of the broader literature indicates that while ESG occupies a central position in port operations, there is a noticeable scarcity of studies that explicitly integrate ESG considerations into critical technical decision-making processes, such as the selection of heavy-duty equipment. This indicates that technical decisions like heavy-duty forklift selection should be re-evaluated through an ESG perspective, and that addressing this gap through advanced methodological frameworks would offer a significant contribution to the literature.

Definition 1. FF sets, which are based on fuzzy logic and defined over fuzzy numbers, can be formally described within a universe $X$ as follows: In such a set, $\mu_{\tilde{A}}(x)$ and $\vartheta_{\tilde{A}}(x)$ represent the membership and non-membership degrees, respectively, while $\pi_{\tilde{A}}(x)$ denotes the indeterminacy degree calculated as $\pi_{\tilde{A}}(x)=\sqrt[ 3]{1-\left(\mu_{\tilde{A}}(x)\right)^3-\left(\vartheta_{\tilde{A}}(x)\right)^3}$. These values satisfy the condition $0 \leq\left(\mu_{\tilde{A}}(x)\right)^3+\left(\vartheta_{\tilde{A}}(x)\right)^3 \leq 1$ for all $x \in X$ [11].

Definition 2. Let $\tilde{A}_1=\left\{\left\langle x, \mu_{\tilde{A}_1}(x), \vartheta_{\tilde{A}_1}(x) \mid x \in X\right\rangle\right\}$ and $\tilde{A}_2=\left\{\left\langle x, \mu_{\tilde{A}_2}(x), \vartheta_{\tilde{A}_2}(x) \mid x \in X\right\rangle\right\}$ be two FF sets. The corresponding definitions of these operations are provided below [12]:

(i) $ \tilde{A}_1 \oplus \tilde{A}_2=\left\{\left(\sqrt[3]{\left(\mu_{\tilde{A}_1}(x)\right)^3+\left(\mu_{\tilde{A}_2}(x)\right)^3-\left(\mu_{\tilde{A}_1}(x)\right)^3\left(\mu_{\tilde{A}_2}(x)\right)^3},\left(\vartheta_{\tilde{A}_1}(x) \vartheta_{\tilde{A}_2}(x)\right)\right) \mid x \in X\right\}$

(ii) $\tilde{A}_1 \otimes \tilde{A}_2=\left\{\left(\left(\mu_{\tilde{A}_1}(x) \mu_{\tilde{A}_2}(x)\right), \sqrt[ 3]{\left(\vartheta_{\tilde{A}_1}(x)\right)^3+\left(\vartheta_{\tilde{A}_2}(x)\right)^3-\left(\vartheta_{\tilde{A}_1}(x)\right)^3\left(\vartheta_{\tilde{A}_2}(x)\right)^3}\right) \mid x \in X\right\}$,

(iii) $\eta \tilde{A}_1=\left\{\left(\sqrt[ 3]{1-\left(1-\left(\mu_{\tilde{A}_1}(x)\right)^3\right)^\eta},\left(\vartheta_{\tilde{A}_1}(x)\right)^\eta\right) \mid x \in X\right\}$ for $\eta>0$,

(iv) $\tilde{A}_1^\eta=\left\{\left(\left(\mu_{\tilde{A}_1}(x)\right)^\eta \cdot \sqrt[ 3]{1-\left(1-\left(\vartheta_{\tilde{A}_1}(x)\right)^3\right)^\eta}\right) \mid x \in X\right\}$ for $\eta>0$.

Definition 3. A score function is utilized to convert FF sets into numerical values. If the score functions of two FF sets yield identical results, an accuracy function is then employed to further distinguish between them. Consider two FF sets, $\tilde{A}_1=\left\{\left\langle x, \mu_{\tilde{A}_1}(x), \vartheta_{\tilde{A}_1}(x) \mid x \in X\right\rangle\right\}$ and $\tilde{A}_2=\left\{\left\langle x, \mu_{\tilde{A}_2}(x), \vartheta_{\tilde{A}_2}(x) \mid x \in X\right\rangle\right\}$. The score function $\left(\mathrm{S}\left(\tilde{A}_1\right)\right)$ for the first FF set $\tilde{A}$ is computed as shown in Eq. (1), whereas its accuracy function $\left(\operatorname{Ac}\left(\tilde{A}_1\right)\right)$ is determined according to Eq. (2) [13].

Definition 4. The computations of the Hamacher t-norm (TN) and t-conorm (TCN) are carried out according to Eq. (3) and Eq. (4), respectively [12]:

herein, $\alpha, \beta \in[0,1]$ and $\tau>0$.

Definition 5. Consider a group of FF sets identified as $\tilde{A}_r=\left\{\left\langle x, \mu_{\tilde{A}_r}(x), \vartheta_{\tilde{A}_r}(x) \mid x \in X\right\rangle\right\}$. The FFHWA aggregation operator is computed by performing Eq. (5) [12]:

The corresponding vector $\eta_l=\left(\eta_1, \eta_2, \ldots, \eta_r, \ldots, \eta_R\right)$ represents the weight vector relation with $\tilde{A}_r$ $(r=1,2, \ldots, R)$. Also, $\eta_r>0$ and $\sum\limits_{r=1}^R \eta_r=1$ and $\tau>0$. The proof was done by Hadi et al. [12].

In this study, which aims to support the sustainable and high-performance selection of forklifts used in CFS operations, a hybrid decision-making framework integrating ESG-based evaluation and expert-driven assessments is proposed. The model combines FF-Hamacher-MEREC-ARLON hybrid method approach to address uncertainty in expert judgments and the multidimensional nature of heavy-duty equipment selection. Within this framework, technical performance indicators, environmental impacts, operator safety, and governance-related factors are simultaneously evaluated through structured expert inputs and fuzzy aggregation techniques. In this way, the forklift selection process is conducted by incorporating both sustainability-oriented criteria and robust computational decision tools, enabling ports to make more reliable, objective, and green-transformation-aligned investment decisions.

In this study, ESG-based assessments are considered quantitative assessments, while expert-focused assessments are considered qualitative assessments. In this framework, the constituents of the decision model are characterized as experts $(E=\left\{E_1, E_2, \ldots, E_k, \ldots, E_K\right\}$ $(k=1,2, \ldots, K))$, quantitative criteria $(U=\left\{U_1, U_2, \ldots, U_b, \ldots, U_B\right\}$ $(b=1,2, \ldots, B))$, qualitative criteria $(V=\left\{V_1, V_2, \ldots, V_d, \ldots, V_D\right\}$ $(d=1,2, \ldots, D))$, overall criteria $(C=\left\{C_1, C_2, \ldots, C_j, \ldots, C_n\right\}$ $(j=1,2, \ldots, n)$; $(B+D=C))$, and alternatives $(A=\left\{A_1, A_2, \ldots, A_i, \ldots, A_m\right\}$ $(i=1,2, \ldots, m))$.

The study was conducted in two stages. In the first stage, expert weights were identified and criterion weights were calculated using the FF-MEREC method. In the second stage, alternatives were ranked using the FF-ARLON method. The application steps of the FF-Hamacher-MEREC-ARLON hybrid method are outlined as follows:

• Stage 1 FF-MEREC method [14]:

Step 1: Table 1 is used to calculate the expert weights. After converting the linguistically expressed expert levels into fuzzy numbers, the score function values are obtained using Eq. (6). The expert weights are calculated by normalizing them using Eq. (7).

Step 2: Experts use Table 2 to linguistically evaluate each alternative according to each qualitative criterion. Linguistic expressions are converted into fuzzy numbers.

Step 3: The assessments made by the experts are aggregated using Eq. (8).

Step 4: The aggregated expert assessments are defuzzified using Eq. (9).

Step 5: The initial decision matrix is created by combining the defuzzified expert assessments with quantitative criteria.

Step 6: The initial decision matrix is normalized using Eq. (10).

Step 7: The overall performance matrix for each alternative is calculated with Eq. (11).

Step 8: The partial performance matrix for each alternative is calculated with Eq. (12).

Step 9: The total absolute deviation matrix for each criterion is calculated with Eq. (13).

Step 10: The criteria weight matrix based on the FF-MEREC method is calculated with Eq. (14).

• Stage 2: Ranking of the alternatives based on FF-ARLON method [16].

Step 1: The elements of the aggregated decision matrix obtained as a result of qualitative criterion evaluations are multiplied by 100, and quantitative criteria are combined to obtain the initial decision matrix.

Step 2: The obtained decision matrix is subjected to two different logarithmic normalization processes using Eq. (15) and (16).

Step 3: Normalized decision matrices are combined using the Heron mean method with Eq. (17).

herein, $\gamma \in[0,1]$ indicates the tradeoff ratio.

Step 4: The resulting aggregated decision matrix is weighted using the criterion weights and Eq. (18).

Step 5: The weighted values are calculated separately for benefit criteria and cost criteria using Eq. (19) and (20).

Step 6: The performance matrix of the alternatives is determined using Eq. (21).

The $\varkappa \in[0,1]$ parameter is obtained by dividing the total number of benefit criteria by the total number of criteria.

Step 7: To rank the alternatives according to the ARLON method, Eq. (22) is used as the final step. This yields the final ranking:

In this study, 10 criteria grouped under 4 main headings are identified as critical for forklift selection in CFS operations. These criteria were defined through a focused literature review and expert insights, reflecting both operational requirements and the sustainability priorities of green port initiatives.

(1) Expert Group

Based on the scope and objectives of this study, a group of ten experts involved in port and logistics operations was consulted to aid the assessment process. These experts were selected due to their professional experience and familiarity with CFS activities, heavy-duty equipment management, and sustainability practices within green port environments. Their contributions assisted in identifying and prioritizing the key technical and ESG-related criteria, as well as supporting the evaluation of forklift alternatives in line with the operational and environmental requirements of modern port systems.

(2) Criteria

In this study, the definition of the criteria for ESG-based forklift selection in CFS operations is grounded in a combination of environmental, social, governance, and technical performance factors. These criteria were identified to reflect both the operational requirements of heavy-duty cargo handling and the sustainability priorities of green port practices.

Emission & Fuel Consumption ($\boldsymbol{C_1}$): Evaluates the environmental burden and operational energy performance of forklifts by considering their power source and the actual energy they consume during lifting and maneuvering tasks. Studies comparing diesel and LPG forklifts show that engine type and operating environment significantly influence overall emission intensity and energy demand, with heavier loads and more dynamic driving patterns leading to higher outputs [17].

Energy Efficiency ($\boldsymbol{C_2}$): Reflects the capability of a forklift’s powertrain and hydraulic subsystems to minimize energy losses and optimize performance during lifting, maneuvering, and braking cycles. Research shows that modern electric and hybrid forklifts increasingly rely on mechatronic innovations---such as regenerative braking, advanced motor control, and optimized energy storage---to recover kinetic and potential energy and reduce overall energy demand [18].

Ergonomics ($\boldsymbol{C_3}$): Design characteristics enhancing operator comfort, visibility, vibration, and noise reduction. Research on human-truck interaction shows that ergonomic shortcomings can increase musculoskeletal load, discomfort, and operational fatigue, directly affecting both performance and safety outcomes [19].

Safety Systems ($\boldsymbol{C_4}$): Safety systems refer to the integration of hardware and control technologies designed to ensure the stability, collision avoidance, and safe trajectory tracking of forklifts during operation [20].

Attachment Flexibility ($\boldsymbol{C_5}$): Attachment flexibility refers to the capability of a forklift to safely integrate and operate with different interchangeable equipment (such as clamps, reel handlers, or side-shifters) while maintaining stability, rated capacity, and safety compliance [21].

Telematics & Traceability ($\boldsymbol{C_6}$): Telematics and traceability involve the integration of IoT sensors, blockchain, and digital platforms to ensure real-time visibility, integrity of data, and traceable documentation of equipment operations and lifecycle performance. Within supply chains, the systems record the movement, usage, and condition of assets, enabling the sharing of transparent and tamper-proof data among stakeholders and, further, ensuring accountability for meeting sustainability goals [22].

Load Capacity (ton) ($\boldsymbol{C_7}$): Represents the nominal lifting capacity of the forklift in tonnes. As highlighted in the literature on advanced forklift selection, load-carrying capacity is one of the primary technical determinants influencing the suitability of a forklift for heavy-duty or height-dependent material-handling tasks, directly shaping both operational performance and safety outcomes [23].

Motor Power (kW) ($\boldsymbol{C_8}$): The rated output power of the engine or electric drive motor. Empirical studies demonstrate that forklifts with higher engine power exhibit improved dynamic performance, particularly during loaded driving, lifting operations, and work cycles requiring rapid acceleration or sustained torque delivery [18].

Overall Width (mm) ($\boldsymbol{C_9}$): Represents the total lateral dimension of the forklift chassis and serves as a key geometric parameter affecting stability, maneuverability, and load-handling dynamics. Dynamic modeling studies indicate that a forklift’s structural dimensions---including its chassis width—directly shape the distribution of forces and oscillations experienced during load lifting, influencing both lateral stability and the behavior of critical components such as the mast, chains, and hydraulic cylinders [24].

Turning Radius (m) ($\boldsymbol{C_{10}}$): Represents the minimum circular space a forklift requires to change direction, and it is widely recognized as one of the key geometric parameters influencing maneuverability, operational safety, and layout efficiency. Research on forklift dynamics identifies the turning radius determining a vehicle’s ability to navigate confined industrial or warehouse aisles, with conventional rear-wheel-steered forklifts exhibiting larger turning paths and increased risks related to stability and overturning during sharp directional changes [25].

(3) Heavy-Duty Forklift Alternatives

In the specific case study addressing the selection of forklifts for CFS operations, the potential equipment alternatives are explained below.

Hyundai 250D-9V ($\boldsymbol{A_1}$): A 25-ton Stage V diesel forklift equipped with a Cummins L9 engine, load-sensing hydraulics, ZF automatic transmission, and Hi-MATE telematics, offering balanced performance, safety, and ergonomics [26].

Hyundai 300D-9VC ($\boldsymbol{A_2}$): A 30-ton heavy-duty diesel model using the same Cummins L9 power unit, designed for higher-load stability with efficient hydraulics and advanced operator comfort features [27].

Kalmar DCG250 ($\boldsymbol{A_3}$): A 25-ton diesel forklift powered by a Volvo D8/Cummins 6.7 engine, featuring Eco Mode fuel savings, EGO ergonomic cabin, and optional Insight telematics for efficient, low-emission operation [28].

Kalmar ECG250 ($\boldsymbol{A_4}$): A 25-ton fully electric lithium-ion forklift with regenerative braking, thermal management, zero local emissions, and built-in Insight connectivity for superior ESG performance [29].

Hyster H40XD-12 ($\boldsymbol{A_5}$): A 40-ton high-capacity diesel forklift using a Cummins QSM11 engine and Spicer TE-30 transmission, providing exceptional durability and attachment versatility but higher fuel consumption [30].

The steps of the FF-Hamacher-MEREC-ARLON hybrid method were sequentially applied to selection of heavy-duty forklifts:

• Stage 1 FF-MEREC method

Expert information and expert weights are shown in Table 3. The linguistic expressions of the alternatives evaluated by experts according to qualitative criteria are presented in Table 4, and their fuzzy number equivalents are presented in Table 5. The combined decision matrix is shown in Table 6. The initial decision matrix to be used in the MEREC method is shown in Table 7. The normalized decision matrix is shown in Table 8. The overall performance values of the alternatives are shown in Table 9. The partial performance values of the alternatives are shown in Table 10. The total of absolute deviations and criterion weights are shown in Table 11.

• Stage 2: Ranking of the alternatives based on FF-ARLON method

The initial decision matrix to be used in the ARLON method is shown in Table 12. The first logarithmic normalization matrix is shown in Table 13. and the second logarithmic normalization matrix is shown in Table 14. The normalized decision matrices obtained are aggregated as shown in Table 15. The weighted decision matrix is shown in Table 16. The cost-weighted and benefit-weighted aggregated normalized matrices are shown in Table 17. The final values and the ranking of the alternatives are shown in Table 18.

Two different sensitivity analysis scenarios were applied to test the robustness of the study. In the first scenario, the steps were repeated by changing the tradeoff ratio value in the ARLON method. The results obtained are shown in Figure 1. In the second scenario, each criterion was removed from the problem one by one, and the steps were repeated. The results obtained are shown in Figure 2. In both scenarios, the best alternative has been determined to be the fourth alternative.

The study was compared with 8 different MCDM methods. The methods used in the comparative analysis are: ALWAS [31], AROMAN [32], ARTASI [33], MABAC [34], MARCOS [35], RAM [36], SAW [37], and WASPAS [38]. The results obtained from the comparative analysis are shown in Figure 3. According to the results obtained, the best alternative in all methods is the same as the ARLON method used in the study and has been determined as the 4th alternative.

The proposed FF-Hamacher-MEREC-ARLON framework was applied to evaluate five forklift alternatives used in CFS operations. The MEREC results indicate that technical performance criteria, particularly motor power, overall width, and turning radius, have the highest importance weights, while ESG indicators show moderate but meaningful influence. Based on these weights, the ARLON ranking method identified Alternative 4 as the best-performing option.

To assess the robustness of the findings, two sensitivity analyses were conducted. In the first scenario, changing the tradeoff ratio within the ARLON method did not alter the ranking results. In the second scenario, each criterion was removed sequentially and the entire process recalculated; Alternative 4 again remained the top-ranked choice. Additionally, comparative analyses with eight established MCDM methods (ALWAS, AROMAN, ARTASI, MABAC, MARCOS, RAM, SAW, and WASPAS) produced identical rankings, further validating the consistency and reliability of the proposed model. Overall, the results demonstrate that the hybrid framework provides stable, reproducible, and sustainability-aligned outcomes for heavy-duty forklift selection.

This study proposes a comprehensive hybrid decision-making framework integrating FF-Hamacher aggregation, the MEREC weighting technique, and the ARLON ranking method to support the sustainable and high-performance selection of heavy-duty forklifts used in Container Freight Station (CFS) operations. The increasing emphasis on environmentally responsible port management, combined with the operational challenges of handling cargo exceeding 25 tons, necessitates a multidimensional evaluation structure that simultaneously addresses technical, environmental, social, and governance-related criteria. The developed model effectively captures expert uncertainty through FF sets, ensures objective determination of criterion weights using the MEREC approach, and provides a robust prioritization of alternatives through the ARLON method.

The empirical results demonstrate that technical performance indicators, particularly lifting capacity, motor power, overall width, and turning radius, constitute the most influential criteria in forklift selection, while sustainability-driven factors such as emission and fuel consumption also play a significant role. Among the evaluated alternatives, Alternative 4 consistently achieved the highest performance score across the primary analysis, sensitivity tests, and comparative assessments. Two extensive sensitivity analyses confirmed that the final ranking remained unchanged under variations in the ARLON tradeoff ratio and the exclusion of individual criteria, reinforcing the stability of the model. Furthermore, comparative analysis with eight established MCDM methods (ALWAS, AROMAN, ARTASI, MABAC, MARCOS, RAM, SAW, and WASPAS) yielded identical results, validating the reliability and methodological soundness of the proposed hybrid approach.

Overall, the findings highlight the strategic importance of incorporating ESG-based indicators into equipment selection processes in modern port operations. The proposed framework not only enhances decision accuracy but also aligns equipment procurement decisions with green port transformation policies. Future research may extend this approach to other types of port equipment, explore dynamic or real-time expert evaluation mechanisms, or integrate life-cycle assessment metrics to further strengthen sustainability-oriented decision-making in maritime logistics.